Real time analysis of periodic structures on semiconductors

ABSTRACT

A system for characterizing periodic structures on a real time basis is disclosed. A multi-parameter measurement module generates output signals as a function of wavelength or angle of incidence. The output signals are supplied to a parallel processor, which creates an initial theoretical model and calculates the theoretical optical response. The calculated optical response is compared to measured values. Based on the comparison, the model configuration is modified to be closer to the actual measured structure. Thereafter, the complexity of the model is iteratively increased, by dividing the model into layers each having an associated width and height. The model is fit to the data in an iterative manner until a best fit model is obtained which is similar in structure to the periodic structure.

This application is a divisional of U.S. Application Ser. No.09/906,290, filed Jul. 16, 2001. Now U.S. Pat. No. 6,704,661.

TECHNICAL FIELD

The subject invention relates to the analysis of data obtained duringthe measurement of periodic structures on semiconductors. In particular,an approach is disclosed which allows accurate, real time analysis ofsuch structures.

BACKGROUND OF THE INVENTION

The semiconductor industry is continually reducing the size of featureson wafers. These features include raised profiles and trenches that havea particular height (or depth), width and shape (contour). Accuratemeasurement of these features is necessary to insure appropriate yields.

Technologies suitable for measuring these small periodic features(critical dimensions) are quite limited. Optical measurement technologyis the most desirable since it is a non-contact technique. However, thesmallest spot size of conventional optical probe beams is larger thanthe size of the periodic features which need to be measured.

FIG. 1 illustrates a substrate 8 having basic periodic pattern 10 formedthereon. The pattern will have a certain characteristic height (H),separation (S) and width (W). Note that in this illustration, the sidewalls of the structure are not vertical, so the width varies over theheight of the structure. FIG. 1 also schematically indicates a probebeam spot 12 which is larger than the spacing between the individualfeatures.

The difficulty in directly measuring such small structures has lead tothe development of scatterometry techniques. These techniques have incommon the fact that light reflected from the periodic structure isscattered and can be treated mathematically as light scattered from agrating. A significant effort has been made to develop metrology devicesthat measure and analyze light scattered from a sample in order toevaluate the periodic structure.

For example, U.S. Pat. No. 5,607,800 discloses the concept of measuringreflected (scattered) light created when a broad band probe beaminteracts with a sample. The reflected light intensity as a function ofwavelength is recorded for a number of reference samples having knownperiodic features. A test sample is then measured in a similar mannerand the output is compared to the output obtained from the referencesamples. The reference sample which had the closest match in opticalresponse to the test sample would be assumed to have a periodicstructure similar to the test sample.

A related approach is disclosed in U.S. Pat. No. 5,739,909. In thissystem, measurements from a spectroscopic ellipsometer are used tocharacterize periodic structures. In this approach, the change inpolarization state as a function of wavelength is recorded to deriveinformation about the periodic structure.

Additional background is disclosed in U.S. Pat. No. 5,867,276. Thispatent describes some early efforts which included measuring the changein intensity of a probe beam as a function of angle of incidence.Measurements at multiple angles of incidence provide a plurality ofseparate data points. Multiple data points are necessary to evaluate aperiodic structure using a fitting algorithm. In the past, systems whichtook measurements at multiple angles of incidence required moving thesample or optics to vary the angle of incidence of the probe beam. Morerecently, the assignee herein developed an approach for obtainingscatterometry measurements at multiple angles of incidence withoutmoving the sample or the optics. This approach is described in U.S. Pat.No. 6,429,943, issued Aug. 6, 2002.

U.S. Pat. No. 5,867,276, like the other prior art discussed above,addresses the need to obtain multiple data points by taking measurementsat multiple wavelengths. This patent is also of interest with respect toits discussion of analytical approaches to determining characteristicsof the periodic structure based on the multiple wavelength measurements.In general, these approaches start with a theoretical model of aperiodic structure having certain attributes, including width, heightand profile. Using Maxwell's equations, the response which a theoreticalstructure would exhibit to incident broadband light is calculated. Arigorous coupled wave theory can be used for this analysis. The resultsof this calculation are then compared to the measured data (actually,the normalized data). To the extent the results do not match, thetheoretical model is modified and the theoretical data is calculatedonce again and compared to the measured data. This process is repeatediteratively until the correspondence between the calculated data and themeasured data reaches an acceptable level of fitness. At this point, thecharacteristics of the theoretical model and the actual sample should bevery similar.

The calculations discussed above are relatively complex even for themost simple models. As the models become more complex (particularly asthe profiles of the walls of the features become more complex) thecalculations become exceedingly long and complex. Even with todays highspeed processors, the art has not developed a suitable approach foranalyzing more complex structures to a highly detailed level on a realtime basis. Analysis on a real time basis is very desirable so thatmanufacturers can immediately determine when a process is not operatingcorrectly. The need is becoming more acute as the industry moves towardsintegrated metrology solutions wherein the metrology hardware isintegrated directly with the process hardware.

One approach which allows a manufacturer to characterize features inreal time is to create “libraries” of intensity versus wavelength plotsassociated with a large number of theoretical structures. This type ofapproach is discussed in PCT application WO 99/45340, published Sep. 10,1999 as well as the references cited therein. In this approach, a numberof possible theoretical models are created in advance of the measurementby varying the characteristics of the periodic structure. The expectedoptical response is calculated for each of these different structuresand stored in a memory to define a library of solutions. When the testdata is obtained, it is compared to the library of stored solutions todetermine the best fit.

While the use of libraries does permit a relatively quick analysis to bemade after the sample has been measured, it is not entirely satisfactoryfor a number of reasons. For example, each time a new recipe is used(which can result from any change in structure, materials or processparameters), an entirely new library must be created. Further, eachlibrary generated is unique to the metrology tool used to make themeasurements. If the metrology tool is altered in any way (i.e. byreplacing an optical element that alters the measurement properties ofthe tool), a new library must be created. In addition, the accuracy ofthe results is limited by the number of models stored in the library.The more models that are stored, the more accurate the result, however,the longer it will take to create the library and the longer it willtake to make the comparison. The most ideal solution would be to developa system which permitted iterative (fitting) calculations to beperformed in real time and which is easily modified to account forchanges in the metrology tool and the process begin monitored.

One approach to speeding up the fitting calculation can be found in U.S.Pat. No. 5,963,329. (The latter patent and the other publications citedabove are all incorporated herein by reference.) This patent discloses amethod of reducing the number of parameters needed to characterize theshape or profile of the periodic structure. In this approach, thestructure is mathematically represented as a series of stacked slabs.The authors suggest that the structure must be divided into bout 20slabs to permit proper characterization of the structure. However, theauthors note that performing an analysis with 40 variables (the widthand height of 20 slabs) would be too computationally complex.Accordingly, the authors suggest reducing the complexity of thecalculation by using sub-profiles and scaling factors. While such anapproach achieves the goal of reducing computational complexity, it doesso at the expense of limiting the accuracy of the analysis. Accordingly,it would be desirable to come up with an approach that was both highlyaccurate and could be performed on a real time basis.

SUMMARY OF THE INVENTION

To address this need, a system has been developed which permits theaccurate evaluation of the characteristics of a periodic structure on areal time basis. In a first aspect of the subject invention, an improvedanalytical approach has been developed for increasing the efficiency ofthe calculations while maintaining a high degree of accuracy. In thisaspect of the invention, a theoretical model of the structure iscreated. This initial model preferably has a single height and widthdefining a rectangular shape. Using Maxwell's equations, the model'sresponse to the interaction with the probing radiation is calculated.The calculated response is compared with the measured result. Based onthe comparison, the model parameters are iteratively modified togenerate a rectangle, which would produce calculated data which mostclosely matches to the measured data.

Using this information, a new model is created with more than one widthand more than one layer. Preferably, a trapezoid is created with threelayers. The model parameters are then adjusted using a fitting algorithmto find the trapezoidal shape which would produce the theoretical datamost closest to the measured data.

Using the results of this fitting process, the model is again changed,increasing the number of widths and layers. The fitting processes isrepeated. The steps of adding widths and layers and fitting the model tothe data are repeated until the level of fitness of the model reaches apredetermined level.

During these iterative steps, the thickness of the layers (density ofthe layers) are permitted to vary in a manner so that a higher densityof layers will be placed in regions where the change in width is thegreatest. In this way, the curvature of the side walls can be mostaccurately modeled.

In this approach, the number of widths and layers is not fixed. It mightbe possible to fully characterize a structure with only a few widths andlayers. In practice, this method has been used to characterizerelatively complex structures with an average 7 to 9 widths and 13 to 17layers.

The scatterometry calculations associated with the early iterations ofthe models (square, trapezoid) are relatively simple and fast. However,as the number of widths and layers increase, the calculations becomeexponentially more difficult.

In order to be able to complete these calculations on a reasonable timescale, it was also necessary to develop a computing approach whichminimized computational time. In another aspect of the subjectinvention, the scatterometry calculations are distributed among a groupof parallel processors. In the preferred embodiment, the processorconfiguration includes a master processor and a plurality of slaveprocessors. The master processor handles the control and the comparisonfunctions. The calculation of the response of the theoretical sample tothe interaction with the optical probe radiation is distributed by themaster processor to itself and the slave processors.

For example, where the data is taken as a function of wavelength, thecalculations are distributed as a function of wavelength. Thus, a firstslave processor will use Maxwell's equations to determine the expectedintensity of light at selected ones of the measured wavelengthsscattered from a given theoretical model. The other slave processorswill carry out the same calculations at different wavelengths. Assumingthere are five processors (one master and four slaves) and fiftywavelengths, each processor will perform ten such calculations to eachiteration.

Once the calculations are complete, the master processor performs thebest fit comparison between each of the calculated intensities and themeasured normalized intensities. Based on this fit, the master processorwill modify the parameters of the model as discussed above (changing thewidths or layer thickness). The master processor will then distributethe calculations for the modified model to the slave processors. Thissequence is repeated until a good fit is achieved.

This distributed processing approach can also be used with multipleangle of incidence information. In this situation, the calculations ateach of the different angles of incidence can be distributed to theslave processor.

Further objects and advantages will become apparent with the followingdetailed description taken in conjunction with the drawings in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is schematic diagram of a periodic surface feature of the priorart.

FIG. 2 is a block diagram of a system for performing the methods of thesubject invention.

FIG. 3 is a simplified schematic of the processor used for performingthe methods of the subject invention.

FIG. 4 is a flow chart illustrating the subject approach for analyzingoptical data to evaluate characteristics of a periodic structure.

FIG. 5 illustrates the shape of the model in a first step of the subjectmethod.

FIG. 6 illustrates the shape of the model in a subsequent step of thesubject method.

FIG. 7 illustrates the shape of the model in a subsequent step of thesubject method.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 2 is a block diagram of a system 16 for performing scatterometrymeasurements on a sample 8 having a periodic structure. For the purposesof this disclosure, the periodic structure means any repeating feature,where the feature size is about the same or smaller than the beam oflight probing the sample such that at least some of the light isscattered rather than specularly reflected.

The system 16 includes a light source 20. As noted above, scatterometrymeasurements are often made using a broad band light source generating aprobe beam 22 having a plurality of wavelengths. As described in U.S.Pat. No. 6,429,943, cited above, the light can also be from a laser. Insuch a case, measurements would be taken as a function of angle ofincidence, preferably without moving the sample.

Probe beam is directed to the sample. Typically a lens (not shown) isused to focus the probe beam to a small spot on the sample. Thereflected probe beam is captured and measured by detector 26. Themeasured intensity of the probe beam will be effected by the amount oflight scattered by the periodic structure. More specifically, theproportion of light diffracted into higher orders varies as a functionof wavelength and angle of incidence such the amount of light redirectedout of the path to the detector also varies thereby permitting thescattering effects to be observed.

The configuration of the detector will be based on the type ofmeasurement being made. For example, a single photodetector can be usedto measure spectroscopic reflected intensity as long as a tunablewavelength selective filter (monochrometer) is located in the path ofthe probe beam. Given the desire to minimize measurement time, aspectrometer is typically used which includes a wavelength dispersiveelement (grating or prism) and an array detector to measure multiplewavelengths simultaneously. An array detector can also be used tomeasure multiple angles of incidence simultaneously. If spectroscopicellipsometry measurements are to be made, the detection system willinclude some combination of polarizers, analyzers and compensators.

The various measurement modalities discussed above are well known in theart and will not be discussed herein. It should be noted that commercialdevices are available that can make multiple measurements. Examples ofsuch devices are described in U.S. Pat. No. 5,608,526 and PCTApplication WO 99/02970, both of which are incorporated herein byreference.

The signals generated by the detector are supplied to a processor 30.The processor 30 need not be physically located near the detector. It isonly required that the measurements from the detector be supplied to theprocessor. Preferably, there is an electrical connection between thedetector and the processor, either directly or through a network. As iswell known to those skilled in the art, the processor will also besupplied with other signals from the system 10 to permit normalizationof the signals. For example, a detector (not shown) would be provided tomeasure the output power of the light source so that variations in theoutput power of the light source could be taken into account during thecalculations.

In the preferred embodiment, and as shown in FIG. 3, the architecture ofthe processor 30 consists of a plurality of microprocessor units linkedby an Ethernet connection. The operating software is arranged to set oneof the processors as a master 32 and the remainder of the processors asslaves 34. The aster handles the higher level functions and distributesthe tasks and retrieves the results from the slaves. Such a system isavailable commercially from Linux Networkx. Inc., headquartered inBluffdale, Utah under the trade name Evolocity™, a registered trademarkof Linux Networkx. In the system used to evaluate the subject invention,an eight processor configuration was selected with each processoroperating at 1.3 gigahertz. When properly combined, the system willoperate at a speed equivalent to about 10 gigahertz. The approach fordistributing the processing tasks will be discussed below.

As noted above, a wide variety of methods have been developed forevaluating characteristics of a periodic structure based on measureddata. The approach described herein falls within the general class ofprocedures where an initial model is created and calculations areperformed to determine the expected response of that sample tointeraction with light. The model is then iteratively modified until theresults of the calculation are close to the actual measured (andnormalized) data. The subject approach can be contrasted with theearlier approaches which required the fabrication of many referencessamples, each of which would be measured, with the results stored forlater comparison to the test sample. This subject approach is alsodifferent from the library approach where large numbers ofconfigurations and their associated optical responses are created andstored for later comparison.

The subject approach recognizes that the shape of the structure can berepresented as a plurality of stacked layers. However, rather thanevaluate the sample based on a preordained, fixed number of layers, thealgorithm is designed to progressively add layers, while seeking a bestfit at each model level. This progression allows a theoretical structurewhich is relatively close the actual structure to be efficientlydetermined. In this approach, only the minimum number of layers which isactually necessary to achieve the desired level of fitness must beanalyzed.

The subject method will be described with respect to the flow chart ofFIG. 4 and the drawings of FIGS. 5 to 7.

In the initial step 102, a rectangular model 50 (FIG. 5) is created.Typically, the model is created using seed values based on the expectedcharacteristics of the sample. For example, the model will includeinformation such as the index of refraction and extinction coefficientof the material. It is possible that this information can be obtained bymeasuring a region of the wafer which is not patterned. The model willalso have a value for the height H₁ and width W₁.

The processor will then calculate the expected intensity that would bemeasured from a sample having a periodic structure with these initialcharacteristics (step 104). For the purposes of this example, it will beassumed that the measured data is obtained from a spectroscopicreflectometer. Accordingly, for each of the measured wavelengths, theprocessor will determine, using Maxwell's equations and a rigorouscoupled wave theory, the expected normalized measured intensity of lightreflected from the theoretical model. In a typical example, ameasurement might include 50 to 100 wavelengths.

Once this calculation has been performed for each of the wavelengths,the results are compared to the normalized measurements obtained fromthe sample (step 106). This comparison can be done with a conventionalleast squares fitting algorithm, for example Levenberg-Marquardt. Theresult of this comparison, will be used to modify the parameters of themodel, in this case the starting height and width (step 108). Theprocessor then calculates the expected intensity of reflected light ateach wavelength from a structure with the modified attributes (step110). These new values are compared to measured values and, ifnecessary, the model is once again modified. In practice, the iterativeprocess usually needs to be repeated some 4 to 8 times before a suitablefit is achieved. The operator can define the desired level of fitness,i.e. when the differences between the model and the actual measurementsas represented by the result of the comparison drops below apredetermined value. The best fit result will be a rectangle which mostclosely approximates the actual periodic structure.

The next step is to modify the model by increasing its complexity (step114). More specifically, the shape or grating profile is changed from asimple rectangle to a trapezoid (see 54 in FIG. 6) having a top width WIand an independent bottom width W₂. In addition, the structure will bedivided in a plurality of rectangular layers, in this case preferablythree. Rather than using a polynomial expansion, the modification ofthis model is done using spline coefficients. The starting point for themodification is the best fit rectangle determined in the previous step.

The grating profile is defined using a class of spline algorithmsincluding the well known cubic spline, Bezier-curves, B-spline and itsmore generalized form of non-uniform rational B-splines (The NURBS Book,by Les Piegl and Wayne Tiller, Springer, 1995). The benefits of such anapproach are that 1) the curves are controlled by a set of controlpoints and 2) the shape described by splines is more flexible than thatdescribed by polynomial expansions. A B-spline curve is described as

C(u)=sum_(j) N _(jp)(u) P _(j)

Where P_(j) are the control points which can be scalars or vectorsdepending the desired flexibility. To minimize the number of fittingparameters for cubic splines, a user has the flexibility to choosedifferent ways to allocate the spline points, in the vertical direction.Assuming that the grating height is scaled between 0 and 1 and assumingthat the points t_(j) are evenly distributed between 0 and 1, we thenuse a sigmoid function of the following form to transform t_(j) tou_(j): as (David Elliot, J. Australian Math. Soc. B40(E), pE77-E137,1998):

u=f ^(n)(t)/(f ^(n)(t)+f ^(n)(1−t))f(t)=t(1+c(1−t)),

where

c=2(n/1−1)

and n>max(1,½).

The effect of this transformation is that the spline points are moredensely allocated at the two ends when n>1. This is very close to howthe nodes are distributed in Gaussian integration. It also correspondsto the more common periodic profiles which have more curvature near thetop and bottom of the structure.

Another aspect of our algorithm involves how the system is divided intoslices or layers (discretized). The simplest approach is to divide thegrating evenly in each material. However, similar to how spline pointsare allocated, we can also discretize the system similar to the Gaussianintegration which is again similar to the Sigmoid function describedearlier. Significantly, we also use the idea of adaptively discretizingthe system according to the curvature of the curve. In this approach, weallow the assignment of layers to be actively varied, along with theother characteristics of the model, during the fitting process. In thisprocess, we define d=∫du|dw/du|, then each segment (between splinepoints) should have d/n slices, where w is the width as a function ofheight u, and n is the total number of slices in the model.

Once the starting parameters are defined, the processor will calculatethe expected intensity for this new structure at each of the measuredwavelengths (step 116). The results are compared to the measured values(step 118) and if the fit is not acceptable, the model is modified(120). In accordance with the subject method, the algorithm is free tomodify the widths and layer thicknesses regardless of the valuesobtained in previous steps. The algorithm is also designed to adjust thelayer thickness such that the greater number of layers will be used todefine regions where the width is changing the fastest. This procedureis repeated until a trapezoid which most closely approximates the actualperiodic structure is determined.

Once the best fit trapezoid is defined, the complexity of the model isagain increased to include one or more widths and layers (Step 130, andFIG. 7). In the preferred embodiment, the model is modified by adding asingle extra width. The number of layers is also increased. Preferably,the number of layers at each iteration is at least 2Y−1 (where Y is thenumber of widths) but no greater than 2Y+1.

The processor will then calculate the expected intensity for this newstructure at each of the measured wavelengths (step 134). The resultsare compared to the measured values (step 136) and if the fit is notacceptable, the model is modified (140). This procedure will repeat inan iterative fashion until the model with the selected number of widthsand layers best fits the data. If that structure meets the overallpredetermined level of fitness, the process is complete and the modelwill suitably match the actual periodic structure (step 142). If not,the processor will loop back (along path 144) to create a new model withadditional widths and layers (See 56 in FIG. 7). In a initialexperiments, the average number of widths and layers needed toadequately characterize a structure was about 7 to 9 widths and 13 to 17layers. With these additional widths and layers, structures with variouswall profiles can be analyzed.

As noted above, one feature of the subject method is its ability topermit the thickness and density of the theoretical layers to varyduring each iteration. It should be noted, however, that some periodicstructures under investigation will include actual physical layers. Ifso, these physical layers can be used as boundaries to further define orconstrain the model.

The calculations required to determine the response of a sample toincident radiation are complex. As the number of widths and layersincreases, the time required to make the calculations increasesdramatically. Accordingly, in a second aspect of the subject invention,the processing tasks are distributed to a parallel processor system.

In the preferred embodiment, one of the eight processors (FIG. 3) isconfigured as the master processor 32 and the remaining seven processorare slaves 34. The master processor controls the overall analysis anddistributes certain of the functions to the slave processors. As notedabove, the most time consuming portion of the calculation is thedetermination of the optical response of the model to a each of thedifferent measured wavelengths or angles of incidence. The comparison ofthese theoretical results with the measured signals and the modificationof the model can, by comparison, be handled relatively quickly.

Therefore, in the preferred embodiment of the subject invention, themaster processor is responsible for distributing the calculations oftheoretical data to the slave processors (such calculations being shownas steps 104, 110, 116 and 134 in FIG. 4). In the preferred embodiment,the master processor would also participate in these calculations.

A maximum reduction in computational time can be achieved if theworkload is evenly distributed. The preferred approach to achieveuniformity is to distribute the wavelength or angle of incidenceinformation serially across the processors. Thus, the first slaveprocessor (in an eight processor system) would be responsible forcalculating the first (shortest) wavelength as well as the ninth,seventeenth, etc. (n+8). The second slave processor would be responsiblefor the second (next shortest) wavelength as well as the tenth,eighteenth, etc. This approach can be used for both spectrophotometryand spectroscopic ellipsometry. A similar approach can be used withmultiple angle of incidence measurements wherein the first, eighth,seventeenth measured angle would be calculated by the first slaveprocessor, etc.

Once each of the calculations are made at each of the wavelengths (orangles), the master processor will compare the results at each of thewavelengths to the normalized measurements at the correspondingwavelengths. The difference will define the level of fitness of theresult and will be used to determine if another iteration is required.The calculations necessary for each iteration of the model are againdistributed to the slave processors in the manner discussed above.

While the subject invention has been described with reference to apreferred embodiment, various changes and modifications could be madetherein, by one skilled in the art, without varying from the scope anspirit of the subject invention as defined by the appended claims. Forexample, it should be apparent that the inventions described herein arenot specifically dependent upon the particular scatterometry approachused to collect the data. Data can be obtained from spectroscopicreflectometers or spectroscopic ellipsometers. It should be noted thatspectroscopic reflectormeters can obtain data from probe beams directedeither at normal incidence or off-axis to the sample. Similarly,spectroscopic ellipsometers can obtain data from probe beams directedeither at normal incidence or off-axis to the sample. Data can also beobtained from multiple angle of incidence devices. As noted in U.S. Pat.No. 6,429,943, applicant has developed a variety of simultaneousmultiple angle of incidence devices that would be suitable. Detaileddescriptions of assignee's simultaneous multiple angle of incidencedevices can be found in the following U.S. Pat. Nos.: 4,999,014;5,042,951; 5,181,080; 5,412,473 and 5,596,411, all incorporated hereinby reference. It should also be understood, that data from two or moreof the devices can be combined to reduce ambiguities in the analysis.Such additional data can be combined in the regression analysisdiscussed above.

See also, U.S. Pat. No. 5,889,593 incorporated by reference. In thispatent, a proposal is made to include an optical imaging array forbreaking up the coherent light bundles to create a larger spot to covermore of the periodic structure.

We claim:
 1. A method of characterizing the surface profile of aperiodic structure based upon optically measured data, said periodicstructure including elements having a vertical height and a width thatcan vary in the horizontal axis, said method comprising the steps of:determining a rectangular model with a theoretical height and width foran element of the periodic structure which provides the best fit withthe optically measured data; deriving a second model using thedetermined rectangular model, the second model having a top widthdifferent from the bottom width and including at least two layers, anditeratively modifying a height and width of each of the at least twolayers to determine the best fit with the optically measured data; andadding additional theoretical intermediate widths and layers to thesecond model in an iterative best fit process until the level of fitnessreaches a predetermined level.
 2. A method as recited in claim 1 whereinthe second model is formulated using spline algorithms.
 3. A method asrecited in claim 2 wherein the second model is with cubic splines.
 4. Amethod as recited in claim 2 wherein spline points are allocated using asigmoid function.
 5. A method of characterizing the surface profile of aperiodic structure based upon optically measured data, said periodicstructure including elements having a vertical height and a width thatcan vary in the horizontal axis, said method comprising the steps of:determining a theoretical height and width of a rectangular model of anelement of the periodic structure which provides the best fit with theoptically measured data; modifying the best fit rectangular model to asecond model having a top width different from the bottom width andincluding at least two layers and determining the best fit with theoptically measured data; and repeating the modifying step by addingadditional theoretical intermediate widths and layers in an iterativebest fit proves until the level of fitness reaches a predeterminedlevel; wherein the second model is formulated using spline algorithms;and wherein the number of slices between spline points is equal to d/n,where d=∫du |dw/du|, w is the width as a function of height u, and n isthe total number of slices in the second model.
 6. A method ofcharacterizing the surface profile of a periodic structure based uponoptically measured data, said periodic structure including elementshaving a vertical height and a width that can vary in the horizontalaxis, said method comprising the steps of: defining a first theoreticalmodel with no more than two different widths and at least one layer andmodifying that theoretical model to find a best fit with the opticallymeasured data for an element of the periodic structure; and derivingsubsequent models each having an increased number of theoretical layersand using a fitting algorithm to iteratively adjust a height and widthof each of the theoretical layers until one of the subsequenttheoretical models defines a structure which approximates theconfiguration of the periodic structure to a predetermined fitnesslevel.
 7. A method as recited in claim 6 wherein the subsequenttheoretical models are formulated using spline algorithms.
 8. A methodas recited in claim 7 wherein the subsequent theoretical models areformulated with cubic splines.
 9. A method as recited in claim 7 whereinspline points are allocated using a sigmoid function.
 10. A method ofcharacterizing the surface profile of a periodic structure based uponoptically measured data, said periodic structure including elementshaving a vertical height and a width that can vary in the horizontalaxis, said method comprising the steps of: defining a theoretical modelwith no more than two different widths and at least one layer andmodifying that theoretical mode to find a best fit with the opticallymeasured data for an element of the periodic structure; and iterativelyincreasing the number of widths and theoretical layers using a fittingalgorithm until the theoretical model defines a structure whichapproximates the configuration of the periodic structure to apredetermine fitness level; wherein the theoretical model is formulatedusing spline algorithms; and wherein the number of slices betweenspline-points is equal to d/n, where d=∫du|dw/du|, w is the width as afunction of height u, and n is the total number of slices in thetheoretical model.
 11. A method of characterizing the surface profile ofa periodic structure based upon optically measured data, said periodicstructure including elements having a vertical height and a width thatcan vary in the horizontal axis, said method comprising the steps of:(a) defining a first theoretical model with a theoretical width andheight for an element of the periodic structure, and calculating theoptical response of the first theoretical model and comparing thatresponse to the optically measured data; (b) iteratively modifying thetheoretical width and height of the first theoretical model andcalculating the optical response of the modified first theoretical modeland comparing that response to the optically measured data until apredetermined level of fitness is achieved; (c) defining a secondtheoretical model with more than one width and more than one layerderived from the modified first theoretical model obtained in step (b)and calculating the optical response of the second theoretical model andcomparing that response to the optically measured data; (d) iterativelymodifying the widths, layer thicknesses and layer locations of thesecond theoretical model and calculating the optical response of themodified second theoretical model and comparing that response to theoptically measured data until a predetermined level of fitness isachieved; and (e) repeating steps (c) and (d) by adding widths andlayers to the modified second theoretical model until the level offitness reaches a predetermined level.
 12. A method as recited in claim11 wherein the step of calculating the optical response of the periodicstructure is performed using a rigorous coupled wave theory.
 13. Amethod as recited in claim 11 wherein the number of layers in themodified second theoretical model is at least 2Y-1 but no greater than2Y+1 where Y is the number of widths.
 14. A method as recited in claim11 wherein each layer in the modified second theoretical model isrectangular in shape.
 15. A method as recited in claim 11 applied to asample including the periodic structure, the sample including more thanone physical layer, wherein during said steps of modifying the modifiedsecond theoretical model, the theoretical layers are constrained by thephysical layer structure of the sample.
 16. A method as recited in claim11 wherein the modified second theoretical model is formulated usingspline algorithms.
 17. A method as recited in claim 16 wherein themodified second theoretical model is formulated with cubic splines. 18.A method as recited in claim 16 wherein spline points are allocatedusing a sigmoid function.
 19. A method as recited in claim 16 whereinthe number of slices between spline points is equal to d/n, whered=∫du|dw/du|, w is the width as a function of height u, and n is thetotal number of slices in the modified second theoretical model.